Plots and graphs for thesis

library(tidyverse)
library(writexl)
library(plyr)

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You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
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Attaching package: ‘plyr’

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    arrange, count, desc, failwith, id, mutate, rename, summarise, summarize

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library(lubridate)

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union

library(plotly)

Registered S3 method overwritten by 'data.table':
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Attaching package: ‘plotly’

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Exchange rate and sentiment: is there a connection?

Before we can start with our machine learning model we need to understand the relationship between the two variables, therefore we should calculate covariance. This measures the direction of a relationship between the two variables.

First step: creating a dataframe from the csv

btc_exchange_rate_history <- read.csv("D:/Suli/Szakdolgozat1/development_n_stuff/aggregated_data.csv") %>%
                             select(-X) %>%
                             mutate(Date = as_date(Date))

Error in `mutate()`:
! Problem while computing `Date = as_date(Date)`.
Caused by error in `as.Date.default()`:
! do not know how to convert 'x' to class “Date”
Backtrace:
  1. ... %>% mutate(Date = as_date(Date))
  8. lubridate::as_date(Date)
 10. base::as.Date.default(x, ...)
 11. base::stop(...)

Second step: plotting the data on a scatterplot

todo: exchange price changehez nézni, nem az árhoz

btc_sent_plot

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

Third step: calculate covariance and correlation

btc_cov

[1] 21506.41

A positive covariance means that the two variables tend to increase or decrease together. Correlation helps us analyze the effect of changes made in one variable over the other variable of the dataset. Now that we know this, we should calculate the strength of the relationship between two, numerically measured, continuous variables.

btc_cor

[1] 0.1836311